# Load gapminder library
library(gapminder)

# View data
head(gapminder)

## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

Problems

1. Recreate the bubble plot for the year 1982². Try to mimic this plot as closely as you can, including all the guides on the plot, labels, and the color palette.

# Load libraries
library(tidyverse)

# Combine Asia and Oceania
levels(gapminder$continent) = c("Africa", "Americas", "Asia", "Europe", "Asia")

# Get 1982 data
gm_1982 = gapminder %>%
  filter(year == 1982) 

# create bubble plot
ggplot(data = gm_1982, aes(x = log2(gdpPercap), y = lifeExp)) +
  annotate("text", x = log2(4000), y = 50, label = "1982", color = "lightgrey", size = 50) +
  geom_point(pch = 21, aes(fill = continent, size = pop)) +
  scale_x_continuous(
    name = "Income (per person GDP/capita, PPP$ inflation-adjusted)",
    breaks = log2(c(500, 1000, 2000, 4000, 8000, 16000, 32000, 64000)),
    labels = c("500", "1000", "2000", "4000", "8000", "16k", "32k", "64k"),
    limits = log2(c(300, 70000))
  ) +
  scale_y_continuous(
    name = "Life expectancy (years)",
    breaks = seq(from = 20, to = 90, by = 10),
    limits = c(20, 92)
  ) +
  scale_fill_manual(
    name = "",
    values = c(
      rgb(0, 213, 233, maxColorValue = 255), 
      rgb(127, 235, 0, maxColorValue = 255), 
      rgb(255, 88, 114, maxColorValue = 255), 
      rgb(255, 231, 0, maxColorValue = 255)
      )
  ) +
  scale_size(range = c(1, 24)) +
  theme_light() +
  theme(
    panel.grid.minor = element_blank()  
  ) +
  annotate("text", x = log2(1200), y = 90.7, label = "INCOME LEVEL 1", color = "lightgrey", size = 2.5, vjust = 0) +
  annotate("text", x = log2(4700), y = 90.7, label = "LEVEL 2", color = "lightgrey", size = 2.5, vjust = 0) +
  annotate("text", x = log2(13700), y = 90.7, label = "LEVEL 3", color = "lightgrey", size = 2.5, vjust = 0) +
  annotate("text", x = log2(42700), y = 90.7, label = "LEVEL 4", color = "lightgrey", size = 2.5, vjust = 0) +
  annotate("point", x = c(log2(2500), log2(8000), log2(25000)), y = 91.3, pch = 18, color = "lightgrey", size = 2) +
  guides(size = FALSE)

2. Create a scatterplot of the life expectancy versus income for the year 1982. But use emoji rather than points to mark the observations in the plot. There are several packages that you could use to do this. Here is one example.

# Install emoGG from github
#remotes::install_github("dill/emoGG")

# Load libraries
library(emoGG)

# create dog plot
ggplot(data = gm_1982, aes(x = log2(gdpPercap), y = lifeExp)) +
  geom_emoji(emoji = "1f436")

# Different emoji for each continent
africa   = gm_1982 %>% filter(continent == "Africa")
americas = gm_1982 %>% filter(continent == "Americas")
asia     = gm_1982 %>% filter(continent == "Asia")
europe   = gm_1982 %>% filter(continent == "Europe")

ggplot(data = africa, aes(x = log2(gdpPercap), y = lifeExp)) +
  geom_emoji(emoji = "1f981") + # Plot africa (lion)
  geom_emoji(data = americas, emoji = "1f98c") + # Plot americas (deer)
  geom_emoji(data = asia, emoji = "1f428") + # Plot asia (koala)
  geom_emoji(data = europe, emoji = "1f426") + # Plot europe (bird)
  theme_light()

3. Try out some features of the {ggtext} package (see here) to ninja-fy your plot (e.g., add images to the labels, color text in the title).

# Load libraries
library(ggtext)

# create emoji plot
ggplot(data = africa, aes(x = log2(gdpPercap), y = lifeExp)) +
  geom_emoji(emoji = "1f981") + # Plot africa (lion)
  geom_emoji(data = americas, emoji = "1f98c") + # Plot americas (deer)
  geom_emoji(data = asia, emoji = "1f428") + # Plot asia (koala)
  geom_emoji(data = europe, emoji = "1f426") + # Plot europe (bird)
  theme_light() +
  labs(
    title = "<b>Fuel economy vs. engine displacement</b><br>
    <span style = 'font-size:10pt'><img src='https://twemoji.maxcdn.com/v/13.0.1/72x72/1f981.png' width='20' />=Africa; <img src='https://twemoji.maxcdn.com/v/13.0.1/72x72/1f98c.png' width='20' />=Americas; <img src='https://twemoji.maxcdn.com/v/13.0.1/72x72/1f428.png' width='20' />=Asia; <img src='https://twemoji.maxcdn.com/v/13.0.1/72x72/1f426.png' width='20' />=Europe</span>"
  ) +
  theme(
    plot.title = element_markdown()
    )

4. Use the {gganimate} package (see here) to mimic the animation on Gapminder.com to show how the plot of life expectancy versus income changes over time.

# Load library
library(gganimate)

# Create plot;
# Assign to object to follow fig.width, etc.
p1 = ggplot(data = gapminder, aes(x = log2(gdpPercap), y = lifeExp)) +
  geom_point(pch = 21, aes(fill = continent, size = pop)) +
  scale_x_continuous(
    name = "Income (per person GDP/capita, PPP$ inflation-adjusted)",
    breaks = log2(c(500, 1000, 2000, 4000, 8000, 16000, 32000, 64000, 128000)),
    labels = c("500", "1000", "2000", "4000", "8000", "16k", "32k", "64k", "128k"),
    limits = log2(c(300, 128000))
  ) +
  scale_y_continuous(
    name = "Life expectancy (years)",
    breaks = seq(from = 20, to = 90, by = 10),
    limits = c(20, 92)
  ) +
  scale_fill_manual(
    name = "",
    values = c(
      rgb(0, 213, 233, maxColorValue = 255), 
      rgb(127, 235, 0, maxColorValue = 255), 
      rgb(255, 88, 114, maxColorValue = 255), 
      rgb(255, 231, 0, maxColorValue = 255)
      )
  ) +
  scale_size(range = c(1, 24)) +
  theme_light() +
  theme(panel.grid.minor = element_blank()) +
  guides(size = FALSE) +
  transition_time(year) +
  labs(title = '{frame_time}')

# Display animation
p1